It is known to utilize telephone call centers to facilitate the receipt, response and routing of incoming telephone calls relating to customer service, retention, and sales. Generally, a customer is in contact with a customer service representative (“CSR”) or call center agent who is responsible for answering the customer's inquiries and/or directing the customer to the appropriate individual, department, information source, or service as required to satisfy the customer's needs.
It is also well known to monitor calls between a customer and a call center agent. Accordingly, call centers typically employ individuals responsible for listening to the conversation between the customer and the agent. Many companies have in-house call centers to respond to customers complaints and inquiries. In many case, however, it has been found to be cost effective for a company to hire third party telephone call centers to handle such inquiries. As such, the call centers may be located thousands of miles away from the actual sought manufacturer or individual. This often results in use of inconsistent and subjective methods of monitoring, training and evaluating call center agents. These methods also may vary widely from call center to call center.
While monitoring such calls may occur in real time, it is often more efficient and useful to record the call for later review. Information gathered from the calls is typically used to monitor the performance of the call center agents to identify possible training needs. Based on the review and analysis of the conversation, a monitor will make suggestions or recommendations to improve the quality of the customer interaction.
Accordingly, there is a need in customer relationship management (“CRM”) for an objective tool useful in improving the quality of customer interactions with agents and ultimately customer relationships. In particular, a need exists for an objective monitoring and analysis tool which provides information about a customer's perception of an interaction during a call. In the past, post-call data collection methods have been used to survey callers for feedback. This feedback may be subsequently used by a supervisor or trainer to evaluate an agent. Although such surveys have enjoyed some degree of success, their usefulness is directly tied to a customer's willingness to provide post-call data.
More “passive” methods have also been employed to collect data relating to a customer's in-call experience. For example, U.S. Pat. No. 6,724,887 to Eilbacher et al. is directed to a method and system for analyzing a customer communication with a contact center. According to Eilbacher, a contact center may include a monitoring system which records customer communications and a customer experience analyzing unit which reviews the customer communications. The customer experience analyzing unit identifies at least one parameter of the customer communications and automatically determines whether the identified parameter of the customer communications indicates a negative or unsatisfactory experience. According to Eilbacher, a stress analysis may be performed on audio telephone calls to determine a stress parameter by processing the audio portions of the telephone calls. From this, it can then be determined whether the customer experience of the caller was satisfactory or unsatisfactory.
While the method of Eilbacher provides some benefit with respect to reaching an ultimate conclusion as to whether a customer's experience was satisfactory or unsatisfactory, the method provides little insight into the reasons for an experiential outcome. As such, the method of Eilbacher provides only limited value in training agents for future customer communications. Accordingly, there exists a need for a system that analyzes the underlying behavioral characteristics of a customer and agent so that data relating to these behavioral characteristics can be used for subsequent analysis and training.
Systems such as stress analysis systems, spectral analysis models and word-spotting models also exist for determining certain characteristics of audible sounds associated with a communication. For example, systems such as those disclosed in U.S. Pat. No. 6,480,826 to Pertrushin provide a system and method for determining emotions in a voice signal. However, like Eilbacher, these systems also provide only limited value in training customer service agents for future customer interactions. Moreover, such methods have limited statistical accuracy in determining stimuli for events occurring throughout an interaction.
It is well known that certain psychological behavioral models have been developed as tools to evaluate and understand how and/or why one person or a group of people interacts with another person or group of people. [The Process Communication Model® (“PCM”) developed by Dr. Taibi Kahler is an example of one such behavioral model. Specifically, PCM presupposes that all people fall primarily into one of six basic personality types: Reactor, Workaholic, Persister, Dreamer, Rebel and Promoter. Although each person is one of these six types, all people have parts of all six types within them arranged like a “six-tier configuration.” Each of the six types learns differently, is motivated differently, communicates differently, and has a different sequence of negative behaviors in which they engage when they are in distress. Importantly each PCM personality type responds positively or negatively to communications that include tones or messages commonly associated with another of the PCM personality types. Thus, an understanding of a communicant's PCM personality type offers guidance as to an appropriate responsive tone or message.] There exists a need for a system and method that analyzes the underlying behavioral characteristics of a customer and agent communication by automatically applying a psychological behavioral model [such as, for example PCM,] to the communication.
Devices and software for recording and logging calls to a call center are well known. However, application of word-spotting analytical tools to recorded audio communications can pose problems. Devices and software that convert recorded or unrecorded audio signals to text files are also known the art. But, translation of audio signals to text files often results in lost voice data due to necessary conditioning and/or compression of the audio signal. Accordingly, a need also exists to provide a system that allows a contact center to capture audio signals and telephony events with sufficient clarity to accurately apply a linguistic-based psychological behavioral analytic tool to a telephonic communication.
The present invention is provided to solve the problems discussed above and other problems, and to provide advantages and aspects not previously provided. A full discussion of the features and advantages of the present invention is deferred to the following detailed description, which proceeds with reference to the accompanying drawings.